Mining for Spectra - The Dortmund Spectrum Estimation Algorithm
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Obtaining the energy spectra of incident particles such as neutrinos or gamma-rays is a common challenge in neutrino- and Air-Cherenkov astronomy. Mathematically this corresponds to an inverse problem, which is described by the Fredholm integral equation of the first kind. Several algorithms for solving inverse problems exist, which are, however, somewhat limited. This limitation arises from the limited number of input observables and the fact that information on individual events is lost and only the unfolded distribution is returned. In this paper we present the Dortmund Spectrum Estimation Algorithm (DSEA), which aims at overcoming the aforementioned obstacles by treating the inverse problem as a multinominal classification task. This modular and highly flexible algorithm can easily be tailored to a problem at hand. To avoid a potential bias on the class distribution used for the training of the learner, DSEA can be applied in an iterative manner using a uniform class-distribution as input.
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